Download Mapping complex disease traits with global gene expression

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Mapping complex disease traits with
global gene expression
William Cookson*, Liming Liang‡, Gonçalo Abecasis‡, Miriam Moffatt* and
Mark Lathrop§
Abstract | Variation in gene expression is an important mechanism underlying susceptibility
to complex disease. The simultaneous genome-wide assay of gene expression and genetic
variation allows the mapping of the genetic factors that underpin individual differences in
quantitative levels of expression (expression QTLs; eQTLs). The availability of systematically
generated eQTL information could provide immediate insight into a biological basis for
disease associations identified through genome-wide association (GWA) studies, and can
help to identify networks of genes involved in disease pathogenesis. Although there are
limitations to current eQTL maps, understanding of disease will be enhanced with novel
technologies and international efforts that extend to a wide range of new samples and tissues.
Genome-wide association
study
(GWA study). An examination
of common genetic variation
across the genome designed to
identify associations with traits
such as common diseases.
Typically, several hundred
thousand SNPs are
interrogated using microarray
or bead chip technologies.
*National Heart and Lung
Institute, Imperial College
London, London SW3 6LY, UK.
‡
Center for Statistical
Genetics, Department of
Biostatistics, SPH II, Ann
Arbor, Michigan
48109‑2029, USA.
§
CEA / Centre National de
Genotypage, 91057 Evry,
France.
Correspondence to W.C., L.L.,
G.A., M.M. or M.L.
e‑mails:
[email protected];
[email protected];
[email protected];
[email protected];
[email protected]
doi:10.1038/nrg2537
Genome-wide association (GWA) studies of common com-
plex or multifactorial diseases have been spectacularly
successful in the last 2 years, with many new loci identified with levels of probability that were once thought
unattainable. However, the extraordinary levels of significance of the association signals have yet to be translated into a full understanding of the genes or genetic
elements that are mediating disease susceptibility at
particular loci.
The functional effects of DNA polymorphism on
multifactorial disease can be mediated through several
mechanisms. Polymorphisms that alter protein function
can have very important effects, such as NOD2 (nucleotidebinding oligomerization domain-containing 2; also
known as CARD15) mutations in inflammatory bowel
disease 1 and FLG (filaggrin) mutations in eczema
(atopic dermatitis)2. However, systematic study of complex diseases with known non-synonymous SNPs has
not yielded many highly significant results3, and variation in gene expression is probably a more important
mechanism underlying susceptibility to complex disease. The abundance of a gene transcript is directly
modified by polymorphism in regulatory elements.
Consequently, transcript abundance might be considered as a quantitative trait that can be mapped with
considerable power. These have been named expression
QTLs (eQTLs)4,5.
There is a substantial gap between SNP associations from a GWA study and understanding how the
locus contributes to disease. Further genotyping and
statistical analyses are often necessary to identify causal
variants, which are then functionally investigated. This
Review explores the value of systematic identification of
eQTLs as one means of characterizing the function
of loci underlying complex disease traits. The combination of whole-genome genetic association studies and
the measurement of global gene expression allows the
systematic identification of eQTLs. By assaying gene
expression and genetic variation simultaneously on
a genome-wide basis in a large number of individuals, statistical genetic methods can be used to map the
genetic factors that underpin individual differences in
quantitative levels of expression of many thousands of
transcripts.
The resulting comprehensive eQTL maps provide an
important reference source for categorizing both cis and
trans effects of disease-associated SNPs on gene expression. In addition to providing information about the
biological control of gene expression, such data aid in
interpreting the results of GWA studies. Once the statistical evidence for association of genetic markers to a
disease trait has been established, genome-wide eQTL
mapping data can be examined to see if the same genetic
markers are also associated with quantitative transcript
levels of one or more genes — such markers are known
as eSNPs. The availability of systematically generated
eQTL information provides immediate insight into a
probable biological basis for the disease associations,
and can help to identify networks of genes involved in
disease pathogenesis.
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Gene expression
Microarray analysis
of transcript levels
in tissue of interest
Genotyping
Genome-wide
genotyping of SNPs
and other variants
eQTLs
Association of genetic
markers with gene
expression
Epigenetic
modifications
Integration with
genome-wide
association studies
Network analysis
Disease
susceptibility
Non-genetic effects
Environmental stimuli
Figure 1 | eQTL mapping. Expression QTL (eQTL) mapping begins
with the
Nature Reviews | Genetics
measurement of gene expression in a target cell or tissue from multiple individuals.
This information is the substrate for investigating the effects of DNA polymorphism
(of any type) on the expression of individual genes. Other factors that can alter
transcription, such as epigenetic CpG methylation, may also be mapped. Network
analyses build upon strong correlations that are present between transcripts, and
allow the identification of modules of genes that mediate complex functions. This
information can then be made available and used to interpret genetic associations
and mapping information from the study of complex disease.
Epigenetic
A mitotically stable change in
gene expression that depends
not on a change in DNA
sequence, but on covalent
modifications of DNA or
chromatin proteins such as
histones.
Heritability
(H2). The heritability of an
individual trait is estimated by
the ratio of genetic variance to
total trait variance, so that 0
indicates no genetic effects on
trait variance and 1 indicates
that all variance is under
genetic control.
The potential of genome-wide eQTL identification
was shown originally in the yeast Saccharomyces cer‑
evisiae6 and then in humans, animals and plants4,7. The
history of eQTL mapping has been comprehensively
reviewed7–9, and will not be described in detail here.
This Review will show how the combination of genetics and global gene expression can be a powerful tool
for systematically unravelling the effects of variation
in transcription on disease. First, we briefly introduce
the principles and current methods of eQTL mapping
and describe the basis of eQTLs. We then explore the
relevance of these results to disease gene identification.
The limits of current eQTL mapping data are discussed,
as are the expected impact of new technologies, international efforts to extend results to new samples and tissues, and how cell lines might be tested with stimuli that
are relevant to disease.
eQTL mapping
In practical terms, the starting point for eQTL mapping
is the measurement of gene expression in a target cell
or tissue from multiple individuals (FIG. 1). This information is the substrate for investigating the effects
of DNA polymorphism (of any type) on the expression of
individual genes. The use of microarray technology to
measure gene expression from many thousand of genes
simultaneously has been a principle driving force for
systematic mapping of eQTLs7. The field is benefiting
from progressively more sophisticated platforms for
such studies, which are described in the later sections
of this Review. Procedures for eQTL mapping are based
on the insight that expression levels can be analysed with
genetic approaches in the same manner as any other
quantitative trait phenotype, such as body weight or
blood lipids. In particular, study designs and statistical
methods that are used traditionally to map QTLs can be
successfully applied to the identification of eQTLs10–12.
Interpretation of eQTL data can then be developed further by the incorporation of additional biological information, such as epigenetic modifications and analysis of
regulatory networks, which are discussed below.
eQTLs are influenced not only by genetic polymorphisms, but also by a range of other biological factors.
These can be dissected systematically, starting with the
measurement of heritability (H2).
Heritability. Family studies have shown that many human
eQTLs are highly heritable13,14. The linkage approach, in
which family members are studied, has been valuable
in demonstrating that genetic factors have widespread
and identifiable influences on eQTLs in humans, and
such studies have provided broad localization for some of
the underlying genetic factors 15,16. GWA mapping
of common genetic variants that underlie eQTLs has
recently become possible owing to the wide availability
of high-throughput and low-cost SNP genotyping. These
results are particularly relevant to disease mapping that
is also focused on common SNPs characterized with
similar SNP arrays. moreover, the interpretation of these
eQTL data relies strongly on methodologies that have
been developed for disease GWA13. For example, a family study of lymphoblastoid cell lines (LCLs) identified
nearly 15,000 traits (each corresponding to an individual
Affymetrix probe) with an estimated H2 > 0.3, indicating
that genetic influences on gene expression seem to be
widespread13. Other studies have similarly described a
high H2 of many eQTLs in LCLs and other tissues4,17,18.
Genetic factors (with both cis-acting and trans-acting
effects; see below), are often identified for eQTLs that
have high H2. For example, in the LCL study mentioned
above13, eQTLs for 81% of traits with H2 > 0.8 could be
mapped to one or more SNPs at genome-wide significance. However, the SNP map on average accounted for
less than 20% of the estimated trait H2, consistent with
results obtained by other studies16. This indicates the
presence of genetic or other factors affecting familial clustering on transcription that are not detectable in these
genetic associations. Factors other than SNPs that might
affect H2 are discussed in more detail below. Further
understanding of disease phenotypes can also be gained
from analysing whether particular types of genes have
more heritable variation in expression level13,19 (BOX 1).
Cis and trans effects. Statistical analyses of eQTLs need
to take into account that the loci identified can influence
gene expression either in cis or in trans. The definition of
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Major histocompatibility
complex
(MHC). A complex locus on
chromosome 6p that
comprises numerous genes,
including the human leukocyte
antigen genes, which are
involved in the immune
response.
Gene Ontology
(GO). A widely used
classification system of gene
functions and other
gene attributes that uses a
standardized vocabulary. The
system uses a hierarchical
organization of concepts (an
ontology) with three organizing
principles: molecular functions
(the tasks done by individual
gene products), biological
processes (for example,
mitosis) and cellular
components (examples include
the nucleus and the telomere).
a cis effect is somewhat arbitrary, but cis-acting eQTLs
are typically considered to include SNPs within 100 kb
upstream and downstream of the gene that is affected by
that eQTL. This definition becomes more problematic in
regions of extended linkage disequilibrium, such as the
major histocompatibility complex (mHC) locus.
Detailed analysis of the position of mapped cis-acting
eQTL effects have shown that these are enriched around
transcription start sites and within 250 bp upstream
of transcription end sites, and they rarely reside more
than 20 kb away from the gene20. Cis-acting variants also
seem to occur more often in exonic SNPs20. Trans effects
are usually weaker than cis effects in humans4,5 and in
rats21, but they are more numerous.
It is not known if trans effects are mostly mediated
through transcription factor variants or through other
mechanisms. ‘master regulators’ are trans-acting factors with multiple effects on gene expression that have
been identified in S. cerevisiae22, in rat tissues21 and in
the human genome5. It is of interest that, at least in yeast,
master regulators are not enriched for transcription factors, and trans-regulatory variation seems to be broadly
dispersed across classes of genes with different molecular
functions22.
Other types of variant. The function of DNA can be altered
by many mechanisms in addition to SNPs. Transcription
can also be modified by copy number variants (CNVs),
insertions and deletions, short tandem repeats and single amino acid repeats23. A systematic investigation of
the effects of CNVs in individuals who are part of the
International Hapmap project showed that SNPs and
CNVs captured 84% and 18%, respectively, of the total
detected genetic variation in gene expression but the signals from the two types of variation had little overlap24.
It has been shown that CNVs in regulatory hot spots in
the malaria parasite genome dictate transcriptional variation25. It has also been observed that small-scale copy
number variation (that is, a single or few copies) can lead
to multiple orders of magnitude change in gene expression
and, in some cases, switches in deterministic control26.
Box 1 | Gene ontology analyses
Many expression QTLs (eQTLs) are highly heritable. Therefore, Gene Ontology (GO)
analyses can be applied to eQTL databases to identify the types of gene that show the
most inherited variation in their levels of expression (at least in the cell type studied,
usually lymphoblastoid cell lines; LCLs). The most highly heritable GO biological
process for eQTLs in LCLs in one study was, unexpectedly, ‘response to unfolded
proteins’, a group containing numerous chaperonins and heat shock proteins. The
individual variation in response to unfolded proteins may be an evolutionary response
to cellular stress, and these genes could be candidates in the study of
neurodegenerative diseases and the ageing processes. Genes that regulate RNA
processing, DNA repair and progression through the cell cycle were also exceptionally
heritable. The evolutionary advantage of individual variation in these genes is unclear.
As expected, genes with significant heritability are also enriched in GO categories of
immune response13,19. These highly heritable immune genes may be of particular value
for the study of infectious and inflammatory diseases. The most heritable traits can be
considered as candidate genes for effects on particular disease traits, but they could
also be studied in large population samples, such as those contained in national
biobanks, to investigate their actions on unexpected phenotypes.
Epigenetic factors. In addition to DNA sequence variants, gene transcription is also modulated by epigenetic
modifications (discussed further in the ‘Limitations of
mapping studies’ section below). For example, non-germ
line epigenetic methylation of CpG residues that regulate gene expression is common in the human genome27.
In a limited study of three chromosomes, 17% of genes
can be differentially methylated in their 5′ uTRs and
approximately one-third of the differentially methylated
5′ uTRs are inversely correlated with transcription27. A
further level of complexity comes from post-translational
modifications of histones that modulate DNA accessibility and chromatin stability to provide an enormous
variety of alternative interaction surfaces for trans-acting
factors (reviewed in ReF. 28).
eQTLs and disease gene mapping
Combining eQTL and GWA studies. One of the most
important consequences of eQTL mapping is the link
that it provides between genetic markers of disease identified in GWA studies and the expression of a specific
gene or genes. In particular, the power of these studies depends upon the identification of specific genetic
markers that are simultaneously associated with disease
and eQTLs, whereas simply comparing differences in
gene expression in cases and controls might not provide
sufficient power to detect important differences with
the available sample sizes. The value of this is illustrated
by several recent investigations in which eQTL analysis
was incorporated directly as a component of the GWA
study design (included in TABLe 1). The number of
GWA studies continues to rise rapidly. In GWA studies
to date, 10–15% of the top hits have affected a known
eQTL in a public data set (TABLe 1). We will therefore
discuss selected instances of these to show the value of
the method.
For example, a recent study generated genome-wide
transcriptional profiles of lymphocyte samples from participants in the San Antonio Family Heart Study, and
showed that high-density lipoprotein cholesterol concentration was influenced by the cis-regulated vanin 1
(VNN1) gene 15. Similarly, a study of post-mortem
brain tissue identified eQTLs affecting the MAPT
(microtubule-associated protein tau) and APOE (apolipoprotein e) genes, which play an important part in
Alzheimer’s disease29.
At the same time as the San Antonio study the results
of a GWA study of asthma13,30 identified a series of SNPs
in strong linkage disequilibrium and spanning more
than 200 kb of chromosome 17q23. The study showed
that these SNPs were strongly associated with the risk of
asthma30. The region of association contains 19 genes,
none of which is an obvious candidate for disease.
examination of eQTL data derived from Affymetrix
Hu133A arrays13,30 on the same families showed that
the disease-associated SNPs had highly significant
(p < 10–22) effects in cis on the expression of one the
genes: ORMDL3 (ORm1-like 3).
This locus illustrates the utility of combining eQTL
and disease mapping studies. Despite the highly significant association with both expression and disease, the
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Table 1 | Disease-linked associations with significant expression QTLs from the literature and public databases
study
Trait
Region candidate gene(s)
Transcript
Transcript
affected by snP region
Gudbjartsson
et al.102*
Height
7p22
Logarithm of
odds (LOD) score
GNA12
7p22
13
11q13.2 Intergenic
CCND1
11q13
7.4
7q21.3
C17orf37
17q21
6.0
HSD17B8
6
6.4
GNA12
LMTK2
NDUFS8
11
6.1
3p14.3
PXK
RPP14
3
9.2
Göring et al.
High-density lipoprotein
cholesterol levels
6q21
VNN1
HDL (serum)
Multiple
sites
8.0
Kathiresan et al.40
Polygenic
dyslipidaemia
20q13
PLTP
PLTP
20q13
16
15q22
LIPC
LIPC
15q22
17
11q12
FADS1, FADS2, FADS3
FADS1
11q12
35
FADS3
11q12
8.0
9p22
TTC39B
TTC39B
9p22
7.0
1p13
CELSR2, PSRC1, SORT1
SORT1
1p13
270
PSRC1
1p13
249
15
CELSR2
1p13
80
12q24
MMAB, MVK
MMAB
12q24
43
1p31
ANGPLT3
DOCK7
1p31
27
ANGPLT3
1p31
11
PTGER4
Libioulle et al.
Crohn’s disease
5p13
Intergenic
5p13
3.0
Barrett et al.36
Crohn’s disease
5q31
OCTN1, SLC22A4, SLC22A5 SLC22A5
5q31
Unknown
Hom et al. *
Systemic lupus
erythematosus
8p23.1
C8orf13, BLK
BLK
8p23.1
20
C8orf13
8p23.1
28
Harkonason et al. * Type 1 diabetes
12q13
RAB5B, SUOX, IKZF4
RPS26
12q13
33
1p31.3
ANGPTL3
DOCK7
1p31.3
16
12q13.2
43.2
37
103
104
Wellcome Trust
Case Control
Consortium105*
Type 1 diabetes
12q13.2 ERBB3
RPS26
Todd et al.106*
Type 1 diabetes
12q13.2 ERBB3
RPS26
12q13.2
30.3
Plenge et al.107*
Rheumatoid arthritis
9q34
TRAF1‑C5
LOC253039
9q34
6.3
Thein et al.
Fetal haemoglobin F
production
6q23.3
Intergenic
HBS1L
6q23.3
6.0
Moffatt et al.30
Childhood asthma
17q21
Intergenic
ORMDL3
17
14
Wellcome Trust
Case Control
Consortium105*
Bipolar disorder
16p12
PALB2, NDUFAB1, DCTN5
DCTN5
16p12
9.2
6p21
NR
HLA‑DQB1
6p21
8.9
HLA‑DRB4
6p21
11
SP140
2q37
8.8
108
Di Bernardo et al.109* Chronic lymphatic leukaemia 2q37
SP140
*Identified through comparison of the National Human Genome Research Institute’s Catalog of Published Genome-Wide Association Studies and the mRNA by
SNP Browser v 1.0.1.
predicted expression differences in cases and controls,
which was averaged over all genotypes, was not expected
to be significant given the sample size: this was in
agreement with the observed results30.
In these data, borderline significant effects were
also observed in the expression of the gene neighbouring ORMDL3, GSDML (gasdermin-like)30. Subsequent
eQTL studies with the Illumina platform and RT-PCR
experiments confirmed that the same SNPs determine
eQTLs with both genes. These results focus attention on
one or both of these genes as probable candidates for a
role in disease pathology. many additional studies are
now underway to investigate the biological functions of
these two genes and their relationship to asthma31,32–35.
Using eQTLs to interpret GWA studies. Such findings
have encouraged the use of these eQTL data as a general tool for interpreting results from GWA studies.
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Human leukocyte antigen
(HLA). A glycoprotein, encoded
at the major histocompatibility
complex locus, that is
found on the surface of
antigen-presenting cells and
that present antigen for
recognition by helper T cells.
Recent analyses of Crohn’s disease (CD) illustrate this
approach36,37. Initially, markers on chromosome 5 were
shown to be strongly associated with CD in one GWA
scan, but their biological effects could not be readily deduced as they reside in a 1.25 mb gene desert.
examination of the LCL eQTLs database showed that
one or more of these polymorphisms act as a long-range
cis-acting factor influencing expression of PTGER4
(prostaglandin e receptor 4),
), a gene that resides approximately 270 kb proximal to the association region37. The
homologue of this gene has been implicated in phenotypes similar to CD in the mouse37,38. Thus, research is
now focused on PTGER4 as a primary candidate gene
for this disease susceptibility locus.
Subsequently, the eQTL approach has been applied
systematically in a meta-analysis of GWA studies of CD,
and several other interesting results have been obtained36.
For example, eQTLs were used to address an outstanding question in CD genetics related to the identification
of the CD susceptibility gene or genes in the cytokine
cluster on chromosome 5q31, where SNPs have an established association with disease39. The disease-associated
SNPs in the meta-analysis of this region were all shown
to be correlated with decreased SLC22A5 (solute carrier
family 22, member 5) mRNA expression levels.
Another CD locus identified in the meta-analysis
coincided with the asthma risk locus on chromosome 17,
in which the disease markers are also correlated with
expression of ORMDL3 and GSDML, as described above.
Thus the same genetic variants contribute to susceptibility to both CD and asthma, possibly by perturbing
expression of one or both of these genes. Several additional examples of eQTLs within CD susceptibility
loci have also been reported36. These co-localizations
greatly exceed the number that would be expected by
chance, suggesting that many of them are indicative
of underlying biological processes involved in disease
susceptibility 36.
Public GWA study results are available at the
National Human Genome Research Institute’s Catalog
of Published Genome-Wide Association Studies.
examination of these results identifies many other disease associations for which eQTL data provide similar
insights (TABLe 1). For example, a recent large study of
polygenic dyslipidaemia identified 30 loci with highly
significant effects on blood lipid measurements40.
examination of gene expression in samples of liver
from 957 subjects allowed highly significant eQTLs
to be identified for 7 of the 30 loci40 (TABLe 1). In some
cases, the eQTL data give genetic evidence to support
a candidate gene for which a role was previously suggested from location and biological hypotheses (such as
GNA12 for height on chromosome 7p22, and BLK and
C8orf13 for auto-immune systemic lupus erythematosis
on 8p23.1). more often the gene expression data identifies different genes or suggests a particular gene from
a number of candidates. examples of this include the
cluster of trans-acting genes from the height locus on
chromosome 7q21.3, the RPS26 gene from the type 1
diabetes locus on 12q13.2, and the DCTN5 gene from
the bipolar disorder locus on chromosome 16p12.1.
Not all examples of eQTL findings are straightforward,
as exemplified by the association reported between the
SH2B1 (SH2B adaptor protein 1) locus and body mass
index (BmI)41. In this study, a missense SNP in SH2B1
was also associated with significant variation in transcript
abundances of EIF3C (eukaryotic translation initiation
factor 3, subunit C) and TUFM (Tu translation elongation
factor, mitochondrial). When mutated, the homologue
of SH2B1 leads to extreme obesity in mice, apparently
because of a failure in proper regulation of appetite. The
authors speculate that the SH2B1 variant has a causal role
but is in linkage disequilibrium with a different variant
that influences EIF3C and TUFM mRNA levels; alternatively, regulation of EIF3C or TUFM mRNA levels could
have a causal role instead of, or in addition to, variation
in SH2B1 (ReF. 41).
eQTL databases. mRNA by SNP Browser v 1.0.1 is a
database of eQTLs from asthma studies13,30 that allows
searches by genes, chromosomal regions and SNPs, and
is a good example of how data from this kind of research
can be examined. VarySysDB is another public database
and contains 190,000 extensively annotated mRNA transcripts from 36,000 loci. VarySysDB offers information
encompassing published human genetic polymorphisms
for each of these transcripts separately. In addition to
SNP effects on transcription, VarySysDB includes
deletion–insertion polymorphisms from dbSNP, CNVs
from the Database of Genomic Variants, short tandem
repeats and single amino acid repeats from H-InvDB and
linkage disequilibrium regions from D-HaploDB23.
MHC locus. Analysis of eQTLs in the mHC locus is of particular interest for studies of diseases in which infection
and autoimmunity is a major component42. Intense study
of the mHC locus over many years has revealed many
genes that are duplicated or polymorphic, and DNA variants in the mHC locus have been associated with more
diseases than any other region of the human genome42.
many disease associations have been attributed to selective
binding of processed antigen to the antigen-presenting
grooves of human leukocyte antigen (HLA) variants.
The results of eQTL studies on the mHC locus must
be interpreted with caution. This is because the high
degree of genetic variability and linkage disequilibrium
across the mHC locus could introduce some spurious
results owing to polymorphism in sequences corresponding to probes used for expression measurements43 (see
below). Nevertheless, global gene expression data has
shown very strong effects of particular SNPs on the level
of expression of the classical mHC antigens HLA‑A,
HLA‑C, HLA‑DP, HLA‑DQ and HLA‑DR (p < 10–20 to
p = 10–30)13. This confirmed the effect of genetic variation
on the level of HLA‑DQ expression observed previously44.
The strength of these effects suggests that associations of
mHC class I and class II polymorphism might depend on
the level of gene transcription as much as restriction of
response to antigen13. A possible example is type I diabetes, in which the functional effects of the long-recognized
association to the class II mHC genes45 have not been
elucidated, despite combined p values of less than 10–100.
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Serial analysis of gene
expression
(SAGe). A method for
quantitative and simultaneous
analysis of a large number of
transcripts. Short sequence
tags are isolated, concentrated
and cloned; their sequencing
reveals a gene expression
pattern that is characteristic of
the tissue or cell type from
which the tags were isolated.
These results suggest that even in this intensively studied region, the investigation of eQTLs could add to our
understanding of the many known genetic associations.
Additional biological interpretation and validation. A
genome exerts its functions not through particular genes
or proteins, but through highly complex networks that
produce a range of responses46. As perturbations of such
networks underlie the pathogenesis of many diseases47,48,
network analysis incorporating eQTL data has recently
provided important novel insights into mechanisms
underlying multifactorial diseases16,17,49 (BOX 2).
extensive investigations of human populations, animal models and cellular systems are required to provide
biological validation of the relationship between specific
genes and multifactorial disease traits, even when the
relationship is identified through eQTL analysis. Given
the substantial effort that is required for validation, careful selection of only the strongest candidates is essential. As shown in the above examples, the combination
of GWA studies and eQTL analysis is a powerful way of
identifying a small number of candidate genes and pathways. With the deployment of new technologies, such as
exon arrays and RNA resequencing, and expansion of
the tissue types covered, as described below, we expect
future eQTL databases to be even more powerful tools
for such identifications.
Box 2 | Networks and other analytical tools
Traditional genetics and cellular biology has rested on the assumption that a single
stimulus (or DNA variant) when applied to a cell (or gene) will have a single outcome.
The reality is that even a simple stimulus will induce changes in transcription in many
genes that interact in complex networks, with an outcome that affects many different
transcripts and processes.
The networks can be considered to be made up of multiple pathways that act at
genetic, genomic, cellular, tissue and whole-organism levels46. The technology that
is already available to gather global information on gene expression, proteins and
metabolites is now allowing the systematic identification of the networks of genes
that interact in disease processes92,93. Analysis of genetic variants that perturb
networks through the effects of expression QTLs (eQTLs) has recently provided
important novel insights into mechanisms underlying multifactorial diseases16,17,49.
This type of analysis may also lead to the systematic identification of transcription
modules94 and the construction of regulatory networks95. The potential of genetic
mapping approaches to identify networks of genes operating on hematopoietic
stem cells96 and immune responses97 are amongst the examples that have been
discussed in the literature.
The impact of combining eQTL analysis with an investigation of gene networks is
shown by the recent detection of genetic variants associated with transcript
abundance of a macrophage-enriched network and obesity-related traits in human
subjects. Parallel studies in mice and humans identified a network module for
obesity-related traits that was enriched for genes involved in the inflammatory and
immune response. eQTL mapping was then used to identify cis-acting genetic variants
associated with this network of genes. The authors characterized these genetic
variants in a large cohort of individuals, and showed statistical enrichment for variants
that were associated with obesity-related biometric traits16. This approach allowed
the identification of genetic variants that had minor individual effects on the trait, but
that can be identified as a group because of the overall perturbation of the network.
Three genes in this network, lipoprotein lipase (Lpl), lactamase β (Lactb) and protein
phosphatase 1-like (Ppm1l), were validated by gene knockouts, strengthening the
association between this network and metabolic disease traits49.
A bibliography and a range of statistical routines for network analysis can be found
on the Weighted Gene Co-expression Network site.
Potential limitations and future directions
Despite the power of eQTL mapping to help identify the
genetic basis of disease, there are many limitations to
current methodologies and potential for considerable
improvements as technologies develop. The best appreciated technical barriers to optimal eQTL mapping are
in the use of microarrays to measure gene expression
(BOX 3). Other problems and their potential solutions are
discussed below.
Comparisons between microarray platforms. It was
assumed that different microarray platforms give
broadly comparable results 50. However, numerous
studies are now showing that the overlap in transcript
detection between platforms is only ~30–40%, whether
considered as presence or absence of detectable transcripts or the absolute level of transcript abundance51–53.
The same level of discordance appears whether comparisons are made between Affymetrix arrays and serial
analysis of gene expression (SAGe)52, Affymetrix and
Illumina arrays50, Affymetrix and Applied Biosystems
arrays53, or across multiple platforms51.
Some of this discrepancy may be because individual genes are commonly interrogated by different
sequences on different platforms. The situation can be
improved when matching of genes is sought using
genomic sequence rather than sequences inferred from
the uniGene database of transcripts54. Concordance
between platforms is improved further when probes
are compared only when they target overlapping transcript sequence regions on cDNA microarrays or gene
chips55.
These discrepancies may follow from the complex and
unpredictable factors that determine hybridization of
particular nucleic acids to complementary array-bound
sequences56,57. In addition, the selection of sequences on
microarrays has been strongly biased to the 3′ end of
genes, simply because public cDNA databases were first
populated with genes identified by 3′ tags.
A consistent conclusion of comparison studies has
been that different platforms provide complementary
results51,52, probably because they are all sampling only
a selected fraction of the total transcriptome from the
cells or tissue under study. The use of multiple platforms
to extract all the expression information from a cell or
tissue is impractical.
New platforms for measuring gene expression. A more
comprehensive measurement of gene expression comes
from arrays that interrogate all known human exons.
Affymetrix have produced global exon arrays58, which
show a high degree of correspondence in terms of fold
changes with their pre-existing ‘classical microarrays’,
suggesting that the additional probe sets on the exon
arrays will provide reliable as well as more detailed
coverage of the transcriptome59. The use of exon arrays
allows the identification of tissue-specific alternative
splicing events as well as significant expression outside of known exons and well-annotated genes60. exon
arrays on other platforms are likely to provide similarly
robust results.
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Box 3 | Pitfalls with microarrays
The use of microarrays to measure gene expression has led directly to the
development of expression QTL (eQTL) analyses. However, the microarray
approaches that underlie most eQTL studies to date provide only partial gene
coverage and have a limited dynamic range for quantitative detection of expression.
Specific problems inherent in the use of these microarrays include: the systematic
bias that can be introduced during sample preparation, hybridization and
measurement of expression; batch to batch variation in array manufacture; and day
to day variation in laboratory conditions98. These types of effects are probably
under-recognized, as exemplified by a report of large-scale differences in gene
expression between ethnic groups98,99. In this case the highly significant differences
in gene expression that the data had suggested between the groups98 were found to
be due to the separate processing of expression measurements in lymphoblastoid
cell lines (LCLs) from subjects of European and Asian ancestry.
Cis-eQTL artefacts can also arise from the overlap of SNPs with transcript probes100.
Alterations in hybridization efficiency owing to the SNP can give an erroneous
impression of differences in transcript abundance attributable to the SNP (and to
other DNA variants with which it is in linkage disequilibrium)100. It has been estimated
that 15% of microarray probes for any given gene will overlap with SNPs that are
polymorphic in the population under study100. However, most coding SNPs in the
human genome are uncommon, and it also seems that measurements of abundances
are robust against mismatches between the probe and RNA sequences101. Although
evidence that these artefacts have an effect has been presented43, it is reassuring that
in a large study in humans Emilsson et al.16 found no evidence of systematic or specific
hybridization artefacts from SNPs in their eQTL data. Nevertheless, important findings
from microarrays need confirmation by specific assays, such as quantitative PCR, that
avoid polymorphic sequences. Statistical methodology to account for batch effects,
polymorphism and other sources of artefact is discussed by Alberts et al.102
Most human studies of eQTLs have been performed in LCLs, primarily because LCLs
were often created as a source of nucleic acids for genetic studies. However,
LCLs can exhibit progressive genomic instability with multiple passages of storage
and re-growth, with the resulting potential for artefacts.
Additive genetic effects
A mechanism of quantitative
inheritance such that the
combined effects of genetic
alleles at two or more gene loci
are equal to the sum of their
individual effects.
many of the problems that are inherent in the use of
microarrays can be solved by massively parallel, ultrahigh-throughput DNA sequencing systems (reviewed
in ReF. 61). These systems allow direct ultra-highthroughput sequencing of RNA, which can then be
mapped back to the genome. Sequencing of RNA provides a generic tool that can support a family of assays
for measuring the genome-wide profiles of mRNAs,
small RNAs, transcription factor binding, chromatin
structure, DNase hypersensitivity and DNA methylation status61. RNA splices may also be effectively
mapped by sequence-based methods.
Despite the formidable promise, ultra-highthroughput sequencing is still not without problems. The
machines can produce terabytes of data daily, and make
profound demands on bioinformatics for data storage
and assembly of reads. Short reads may pose severe problems for the interpretation of transcripts arising from
gene families with high homology or repetitive regions
of the genome. Nevertheless, it can be anticipated that
within 2 years many studies will rely on this technology,
and that alternative or complementary approaches, such
as large-scale real-time PCR-based expression assays (for
example, as described by Watson et al.62 and developed
by WaferGen), will continue to evolve.
Limitations of mapping studies. As discussed in the section on heritability, currently mapped loci account for
only a portion of the estimated heritability of eQTLs. A
similar degree of unattributed or ‘dark’ heritability has
been observed in GWA studies of common complex
traits and diseases. A large GWA meta-analysis, for
example, recently identified 20 variants that are significantly associated with adult height. The combined
effects of the 20 SNPs explained only 3% of height
variation, taking into account such factors as age and
population63. Similarly, a large GWA meta-analysis of
Crohn’s disease identified 32 loci that significantly affect
the disease, which together explained only 10% of the
overall variance in disease risk and 20% of the genetic
risk36.
A large portion of the unattributed heritability is
expected to result from the effects of multiple loci that
are too weak to detect using current sample sizes18.
This explanation would be consistent with data in
yeast, in which only 3% of highly heritable transcript
abundances are explained by single-locus (monogenic)
inheritance and 50% are consistent with more than five
controlling loci of equal effect 64. Although current SNP
arrays provide relatively comprehensive coverage of the
genome (more than 80%), some of the unattributed
heritability will be due to genetic factors that reside in
unmapped regions, or to variation that is not effectively
tagged at present, such as CNVs. Dominance and interaction effects may also account for some of the unattributed heritability, as these may be confounded with
additive genetic effects in the heritability estimates
with some study designs.
A previously described global eQTL study was based
on sibling pairs, allowing estimates of heritability for all
the transcripts measured13. The study suggested that
dominance had a minimal effect on gene transcription 13. Interestingly it seemed that genetic interactions may have important influences on regulation of
expression for some genes, but inclusion of interaction
effects had a minimal impact on the overall attributable
heritability 13.
epigenetic modifications and other factors that
affect transcript abundance might not be accounted
for in SNP-based association studies (see ‘The basis
of eQTLs’ section above). Genomic imprinting is a
particular case of an epigenetic effect with a parent
of origin-dependent pattern. monoalleleic expression
is established at imprinted loci, via epigenetic marks
transmitted through the germ line. Several common
complex diseases exhibit parent-of-origin effects
that might indicate underlying imprinting, including
asthma65, type I diabetes66,67, rheumatoid arthritis68,
psoriasis69, inflammatory bowel disease70 and selective
immunoglobin A deficiency 71, but systematic analysis of
parent-of-origin effects in eQTL data has not yet been
reported.
Finally, transcript abundance is a function of transcript stability as well as transcript production. many
factors mediate transcript stability, particularly in trans,
either through protein–RNA interaction or through
mechanisms mediated by small interfering RNAs
(siRNAs)72. It seems clear that future studies of disease
susceptibility as well as eQTLs will need to take these
mechanisms into account.
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Gene expression in tissues. Although RNA for eQTL
analyses would ideally be obtained from a wide variety of tissues, most human studies of eQTLs have been
performed in LCLs, primarily because LCLs were often
created as a renewable source of nucleic acids for genetic
studies. Gene expression in LCLs, however, represents the
particular circumstances of epstein–Barr virus infection
of B-cells and their subsequent uncontrolled growth. LCLs
may also exhibit extreme clonality with random patterns
of monoallelic expression in single clones73.
Although only 60% of genes from any particular cell type
will also be found in LCLs4,13, it has been established that
LCLs provide information about gene expression for some
genes that do not function primarily in these cells4,74–76.
In addition, a recent comparison of eQTLs derived from
the analysis of blood and adipose tissue showed little difference in the number of eQTLs that could be mapped,
and there was an approximately 50% overlap of mapped
loci from the two RNA sources16. Similarly, comparison
between four different tissues showed no statistically significant differences in the number of mapped transcripts
in experiments involving mapped recombinant inbred
strains of mice18.
Despite the continued utility and convenience of
LCL studies of gene expression, it is evident that many
of the transcripts expressed in LCLs may be housekeeping genes, and transcripts that determine specialized cell
functions and that modify disease may be more parsimoniously distributed. In addition, LCLs are removed from
the stimuli that can induce disordered gene transcription
in disease. This is exemplified by the differences that are
observed in gene expression between LCLs derived from
asthmatics and genes known to be expressed in asthmatic
airways30. These factors all indicate that the direct examination of tissues that are involved in disease can provide
much more information than the LCL alone.
Some eQTL studies of human tissue have already been
carried out, notably of liver 17, adipose16,49 and brain29 tissue. These show that approximately 60% of the transcriptome is expressed in each tissue, and that eQTLs from
these tissues may be a valuable source of information for
genetic mapping. Data from animal models suggest that
tissue samples can allow detection of trans-eQTLs that are
important in determining the composition of individual
tissues18. Tissue samples will also allow the use of network
analyses to identify the complex interactions that may
underlie disease16,17,49 (BOX 2).
The costs of reagents and the limited availability of
appropriate tissues have to date restricted studies in
humans to several hundreds of subjects. Although a formal evaluation of optimal study sizes is difficult because of
unknown trait heritability, we know empirically that studies with a few hundred subjects have consistently identified numerous eQTLs with vanishingly small p values4,5,75.
It is also clear that subtle effects, particularly in trans,
would be detected more reliably with larger samples.
It is therefore timely that the promise of eQTLs as a
tool for disease genetics has been sufficiently exciting to
prompt a National Institutes of Health (NIH) proposal
for the ambitious Genotype-Tissue expression (GTex)
project, a database that might include 1,000 samples from
each of 30 different tissues. The GTex project is currently
running as a 2-year pilot study with the primary goal of
testing the feasibility of collecting high-quality RNA and
DNA from multiple tissues from approximately 160 donors
identified through low post-mortem interval autopsy or
organ transplant settings. If the pilot phase proves successful, the project will be scaled up to involve approximately
1,000 donors, with the eventual creation of a database
to house existing and GTex-generated eQTL data.
The use of tissues poses a number of problems that
need to be resolved. Normal and diseased tissue samples
can be difficult to access, and their use requires careful
attention to ethical, legal and social issues. Samples taken
at post-mortem from many tissues robustly retain their
histological architecture and contain RNA that can be
of sufficient quality for measurements of gene expression. However, the changes in gene expression that might
accompany death or surgical resection have not yet been
documented in any detail. Tissues typically consist of different cell types, and their composition can vary inconsistently in the presence of disease. Finally, tissue-specific
DNA methylation profiles may affect 20% of genes27,
and are expected to be important in understanding
tissue eQTLs.
Although some of these problems may be expected to
degrade the information available from the study of any
particular tissue, it should be appreciated that they will not
systematically lead to false positives in eQTL analyses17,
emphasizing the robustness of the eQTL approach.
Exercising the genome. Tissue biopsies and other samples
extend the ‘expression space’ that can be examined by
eQTL studies. They nevertheless still have limitations for
functional analyses (particularly in humans as opposed
to model organisms) when compared with cells that can
be grown freely in culture and manipulated by systematic
knock downs.
Although the transcripts in a particular cell under particular conditions reflect only part of the function of a
particular genome, the range of transcripts from a given
cell type can be widened by stimulating the cell in a variety
of ways. The experimental extension of the genome expression space has been called ‘exercising the genome’77, and
this strategy can be used to learn much more about gene
expression and integrated gene functions. experimentally,
evidence is already emerging that environmental actions
on gene expression are profound in humans78 and model
organisms79,80 (reviewed in ReF. 81), and it is reasonable to
assume that these components of gene expression can be
fruitfully accessed through exposure to relevant stimuli.
It is interesting that, in model organisms, environmentally
induced changes in gene expression seem to act through
prominent trans effects79,80 that may not be present in
unstressed cells and tissues.
It is therefore desirable that the genome of human
LCLs or primary cells of particular interest be exercised
by stimulating their gene expression in different ways.
model stimuli that could be tested in these systems
include pro-inflammatory stresses, metabolic stresses
(such as high or low glucose, or hypoxia), the response
to radiation, the response to signalling molecules (such
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Box 4 | eQTLs and network analyses of cancer
Mutations that disrupt cell growth control mechanisms are a feature of cancer. In
addition, the unchecked cell division that is characteristic of cancer can in time result
in many secondary mutations and progressive genomic disorganization103. Genetic
studies of cancer tissue (so called somatic cell genetics) have been used to identify the
most common mutations in various tumours. Global gene expression studies have also
been used in many cancer types, typically to identify gene signatures that can predict
the clinical outcome104. However, most signature-based outcome predictions have not
been replicated by independent studies104, perhaps owing to the innate heterogeneity
of cancerous tissue and the problems of deriving statistically stringent results from the
measurement of thousands of transcripts in limited numbers of samples. Expression
QTL (eQTL) analyses are a powerful tool to identify the functional consequences of
the numerous copy number variants (CNVs), deletions and epigenetic modifications
that are a feature of neoplastic cells. eQTL mapping allows the identification not only
of genes underlying malignant processes105 but also of genes modifying disease
progression106 and genes modulating individual responses to chemotherapy107.
Network analyses have not yet been widely applied to the study of cancer, but they
have already led to interesting findings, such as the identification of the ASPM gene
as a molecular target in patients with glioblastoma. The application of network
analyses to cancer eQTLs may be expected to greatly alleviate problems with
multiple comparisons and to lead to easier biological interpretation of results108,109.
Direct comparison of the transcript network architecture of cancerous tissue against
normal tissues may also allow much deeper understanding of cancer biology.
as neurotransmitters, hormones and peptides) and the
response to therapeutic and chemotherapeutic agents.
Conclusions
It is now well established that transcript abundances of
genes may be considered as quantitative traits that can
be mapped with considerable power, and that assaying
gene expression and genetic variation simultaneously on
a genome-wide basis in a large number of individuals will
provide valuable tools for identifying the function of previously mapped susceptibility alleles underlying common
complex diseases.
Although eQTLs are shown to be effective in mapping
complex traits, there are many levels of information that
are inherent in the measurement of global gene expression
that have yet to be accessed, such as the effects of transcript stability, epigenetic effects or environmental stimuli.
In addition, larger studies involving thousands of subjects
may be necessary to identify weak trans effects with the
same precision as the more powerful effects that are often
observed in cis. Although trans effects can be relatively
weak, the genes they modify (the trans-transcriptome) are
likely to contain master regulators with wide effects on
key processes that might feature more strongly in tissues
or in cells subjected to particular environmental stimuli.
many genes are only expressed in particular tissues or at
specific times during development. Thus, although systematic studies of eQTLs are already being planned for
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Acknowledgements
The work was supported by the Wellcome Trust and the EC
funded GABRIEL project, the French Ministry of Research and
Higher Education and by grants from the National Institutes
of Health.
FURTHER INFORMATION
Liming Liang’s homepage:
http://www.sph.umich.edu/csg/liang
Abecasis laboratory homepage (contains programs for
genome-scale data analysis):
http://www.sph.umich.edu/csg/abecasis
Catalog of Published Genome-Wide Association Studies:
http://www.genome.gov/gwastudies
Database of Genomic Variants:
http://projects.tcag.ca/variation
dbSNP: http://www.ncbi.nlm.nih.gov/projects/SNP
D-HaploDB: http://orca.gen.kyushu-u.ac.jp
Genotype-Tissue Expression (GTEx):
http://nihroadmap.nih.gov/GTEx
H-InvDB: http://www.h-invitational.jp
mRNA by SNP Browser v 1.0.1:
http://www.sph.umich.edu/csg/liang/asthma
UniGene: http://www.ncbi.nlm.nih.gov/unigene
VarySysDB:
http://www.h-invitational.jp/varygene/home.htm
WaferGen: http://www.wafergen.com
ALL Links ARe AcTive in The OnLine PDf
www.nature.com/reviews/genetics
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